SoroushVahidi/scidocs huggingface dataset:
Client-Centred Therapy
(2009)

Our thinking in this area has been influenced by the developments in therapeutic thinking in general, but particularly by the evolution of client-centered therapy. The client-centered orientation was, at first, characterized by a rather specific technique or method: the therapist consistently responded by “reflecting” the client’s feeling. This meant that the therapist avoided diagnosis and deductions about the client, or interpretations of hidden meanings the client did not intend. Instead, the therapist attempted always to state in his own words what he sensed to be the client’s intended message, the client’s perception of his feelings, situation, difficulties, and the like. In this effort the therapist tried to sense not merely what was on the surface but the deeply felt meaning which the client only haltingly approached in what he said. Such responding, when successful, meant that the client’s own presently felt message was more deeply understood and responded to than he would have thought possible.
Simple and Effective Multi-Paragraph Reading Comprehension
(2018)

We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA.
Principles of interleukin (IL)-6-type cytokine signalling and its regulation.
(2003)

The IL (interleukin)-6-type cytokines IL-6, IL-11, LIF (leukaemia inhibitory factor), OSM (oncostatin M), ciliary neurotrophic factor, cardiotrophin-1 and cardiotrophin-like cytokine are an important family of mediators involved in the regulation of the acute-phase response to injury and infection. Besides their functions in inflammation and the immune response, these cytokines play also a crucial role in haematopoiesis, liver and neuronal regeneration, embryonal development and fertility. Dysregulation of IL-6-type cytokine signalling contributes to the onset and maintenance of several diseases, such as rheumatoid arthritis, inflammatory bowel disease, osteoporosis, multiple sclerosis and various types of cancer (e.g. multiple myeloma and prostate cancer). IL-6-type cytokines exert their action via the signal transducers gp (glycoprotein) 130, LIF receptor and OSM receptor leading to the activation of the JAK/STAT (Janus kinase/signal transducer and activator of transcription) and MAPK (mitogen-activated protein kinase) cascades. This review focuses on recent progress in the understanding of the molecular mechanisms of IL-6-type cytokine signal transduction. Emphasis is put on the termination and modulation of the JAK/STAT signalling pathway mediated by tyrosine phosphatases, the SOCS (suppressor of cytokine signalling) feedback inhibitors and PIAS (protein inhibitor of activated STAT) proteins. Also the cross-talk between the JAK/STAT pathway with other signalling cascades is discussed.
Shaping particle simulations with interaction forces
(2014)

classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2960-6/14/08 Shaping Particle Simulations with Interaction Forces
Kernel estimation for motion blur removal using deep convolutional neural network
(2017)

Blind deblurring can restore the sharp image from the blur version when the blur kernel is unknown, which is a challenging task. Kernel estimation is crucial for blind deblurring. In this paper, a novel blur kernel estimation method based on regression model is proposed for motion blur. The motion blur features are firstly mined through convolutional neural network (CNN), and then mapped to motion length and orientation by support vector regression (SVR). Experiments show that the proposed model, namely CNNSVR, can give more accurate kernel estimation and generate better deblurring result compared with other state-of-the-art algorithms.
New Comparative Study Between DES, 3DES and AES within Nine Factors
(2010)

--With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, also the internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information .Besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information. , which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question. Cryptography considers one of the techniques which used to protect the important information. In this paper a three algorithm of multimedia encryption schemes have been proposed in the literature and description. The New Comparative Study between DES, 3DES and AES within Nine Factors achieving an efficiency, flexibility and security, which is a challenge of researchers.
Distributed Packet Inspection By Using PIL-2 Language
(2013)

Packet Inspection language PIL-2 incorporates directly support for distributed processing into the language.PIL-2 supports generic header filter, payload scanning, packet replication and traffic flow in network. By distributing PIL-2 based on the possibility created for heterogeneous network of processing node, which gives monitoring architecture facilities with high speed packet processing. The result shows that perform complex processing at terra bit speeds. This proposed frame work can be used to execute distinct task as load balancing, traffic monitoring, firewalls and IDS at high bandwidth links. Keywords—PIL-2,Load balancing, IDS, traffic monitoring.
The Digital Sin City: An Empirical Study of Craigslist's Impact on Prostitution Trends
(2016)

Online prostitution has flourished due to presence of online intermediaries. Despite the illegality of selling sexual services online, the Section 230 of Communications Decency Act shields websites from liability for unlawful postings by third parties. Consequently, the websites like Craigslist have become a haven for prostitution-related ads. In this paper, we quantify the economic impact of Craigslist’s entry on prostitution incidence, and identify potential pathways in which the website affects the sex industry. Using a national panel data for 1,796 U.S. counties from 1999 to 2008, our results suggest that entry of Craigslist to a county is related to 26.61 percent increase in prostitution cases. In addition, our analyses investigates four key market mechanisms, namely the relationship to commercial vice, geographical trends, spillover effects, and enhanced policing efficacy. Our results contributes broadly to the emerging literature that researches the societal challenges associated with online intermediaries and Internet penetration.
Zero-Knowledge Proofs of Knowledge Without Interaction (Extended Abstract)
(1992)

A zero-knowledge proof system of knowledge is a protocol between two parties called the prover and the verifier. The prover wants to convince the verifier that he “knows” the proof of a given theorem without revealing any additional information. This is different from a zero-knowledge proof system of membership where the prover convinces the verifier only of the veridicity of the statement. Zero-knowledge proofs of knowledge are very useful tools in the design of secure protocols. Though, the concept of a proof of knowledge is a very subtle one and great care is needed to obtain a satisfying formalization. In this paper, we investigate the concept of a zero-knowledge proof of knowledge in the noninteractive model of [5, 61. Here, the prover and the verifier share a short random string and the only communication allowed is from the prover to the verifier. Although this is a simpler model than the interactive one, still formalizing zeroknowledge proofs of knowledge is a delicate task. The main results of the paper are the following e We present formal definitions for the concept of non-interactive zero-knowledge proofs ‘Dipartimento di Informatica ed Applicazioni, Universiti di Salerno, 84081 Baronissi (SA), Italy. Partially supported by Italian Ministry of Universit.y and Research (M.U.R.S.T.) and by National Council for Research (C.N.R.) under grant 91.02326.CT12. Email: ads@udsab.dia.unisa.it ‘Aiken Computation Laboratory, Harvard University, Cambridge, MA 02138, USA and Dipart,imento di Informatica, Universiti di Salerno. Supported at Harvard by NSF Grant # NSF-CCR-90-07677. Email: persiano@endor.harvard.edu GIUSEPPE PERSIANO+
Parsing C/C++ Code without Pre-processing
(2009)

It is difficult to develop style-preserving source-to-source transformation engines for C and C++. The main reason is not the complexity of those languages, but the use of the C pre-processor (cpp), especially ifdefs and macros. This has for example hindered the development of refactoring tools for C and C++. In this paper we propose to combine multiple techniques and heuristics to parse C/C++ source files as-is, while still having only a few modifications to the original grammars of C and C++. We rely on the fact that in most C and C++ software, programmers follow a limited number of conventions on the use of cpp which makes it possible to disambiguate different situations by just looking at the context, names, or indentation of cpp constructs. We have implemented a parser, Yacfe, based on these techniques and evaluated it on 16 large open source projects. Yacfe can on average parse 96% of those projects correctly. As a side effect, we also found mistakes in code that was not compiled because it was protected by particular ifdefs, but that was still analyzed by Yacfe. Using Yacfe on new projects may require adapting some of our techniques. We found that as conventions and idioms are shared by many projects, the adaptation time is on average less than 2 hours for a new project.
Effects of hippotherapy and therapeutic horseback riding on postural control or balance in children with cerebral palsy: a meta-analysis.
(2011)

AIM
This research review and meta-analysis presents an overview of the effects of hippotherapy and therapeutic horseback riding (THR) on postural control or balance in children with cerebral palsy (CP).


METHOD
To synthesize previous research findings, a systematic review and meta-analysis were undertaken. Relevant studies were identified by systematic searches of multiple online databases from the inception of the database through to May 2010. Studies were included if they fulfilled the following criteria: (1) quantitative study design, (2) investigation of the effect of hippotherapy or THR on postural control or balance, and (3) the study group comprised children and adults with CP. The selected articles were rated for methodological quality. The treatment effect was coded as a dichotomous outcome (positive effect or no effect) and quantified by odds ratio (OR). The pooled treatment effect was calculated using a random-effects model. Meta-regression of the effect size was performed against study covariates, including study size, publication date, and methodological quality score.


RESULTS
From 77 identified studies, 10 met the inclusion criteria. Two were excluded because they did not include a comparison group. Therapy was found to be effective in 76 out of 84 children with CP included in the intervention groups. The comparison groups comprised 89 children: 50 non-disabled and 39 with CP. A positive effect was shown in 21 of the children with CP in the comparison group regardless of the activity undertaken (i.e. physiotherapy, occupational therapy, sitting on a barrel or in an artificial saddle). The pooled effect size estimate was positive (OR 25.41, 95% CI 4.35, 148.53), demonstrating a statistically significant effectiveness of hippotherapy or THR in children with CP (p<0.001). Meta-regression of study characteristics revealed no study-specific factors.


INTERPRETATION
The eight studies found that postural control and balance were improved during hippotherapy and THR. Although the generalization of our findings may be restricted by the relatively small sample size, the results clearly demonstrate that riding therapy is indicated to improve postural control and balance in children with CP.
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
(2017)

In this paper, we investigate the robustness of traffic sign recognition algorithms under challenging conditions. Existing datasets are limited in terms of their size and challenging condition coverage, which motivated us to generate the Challenging Unreal and Real Environments for Traffic Sign Recognition (CURE-TSR) dataset. It includes more than two million traffic sign images that are based on real-world and simulator data. We benchmark the performance of existing solutions in realworld scenarios and analyze the performance variation with respect to challenging conditions. We show that challenging conditions can decrease the performance of baseline methods significantly, especially if these challenging conditions result in loss or misplacement of spatial information. We also investigate the effect of data augmentation and show that utilization of simulator data along with real-world data enhance the average recognition performance in real-world scenarios. The dataset is publicly available at https://ghassanalregib.com/cure-tsr/.
The KeY Platform for Verification and Analysis of Java Programs
(2014)

The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.
Forecasting Foreign Exchange Rates Using Recurrent Neural Networks
(1996)

This article proposes the use of recurrent neural networks in order to forecast foreign exchange rates. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series. In particular, recurrent networks, in which activity patterns pass through the network more than once before they generate an output pattern, can learn extremely complex temporal sequences. Three recurrent architectures are compared in terms of prediction accuracy of futures forecast for Deutsche mark currency. A trading strategy is then devised and optimized. The profitability of the trading strategy, taking into account transaction costs, is shown for the different architectures. The methods described here, which have obtained promising results in real-time trading, are applicable to other markets.
Deciphering Severely Degraded License Plates
(2017)

Extremely low-quality images, on the order of 20 pixels in width, appear with frustrating frequency in many forensic investigations. Even advanced de-noising and super-resolution technologies are unable to extract useful information from such lowquality images. We show, however, that useful information is present in such highly degraded images. We also show that convolutional neural networks can be trained to decipher the contents of highly degraded images of license plates, and that these networks significantly outperform human observers. Introduction Recognizing text in images is a well-studied problem [1]. Text recognition can either be done by recognizing individual characters or by recognizing the full word. For degraded images, however, it is difficult to localize and recognize individual characters in an image. Word recognition, therefore, has become central to text recognition in degraded images. Deep convolutional neural networks [2] have been used for text recognition in natural images. Goodfellow et al. [3] used a deep neural network to localize, segment and recognize multiple digits on street view images. Jaderberg et al. [4] also proposed an end to end text recognition system for natural images using a deep neural network. Jaderberg et al. used a large word dictionary and formulated the text recognition task as a large scale classification problem. Recently, Svoboda et al. [5] used CNN to remove motion blur from images of license plate blurred with a blur kernel of various directions and lengths. Although this approach is able to deblur highly blurred images, it does not contend with extremely low resolution and noisy images. Unlike much of this previous work we focus on extracting text from highly degraded images on the scale of only a few pixels per character. Hsieh et al. [6] were the first to show that information can be extracted from highly degraded license plates. In this work the authors assume a known font type, font size, and character layout, and assume that the degraded image is blurry and perspectively distorted, but does not necessarily contain additive noise. Although the authors only show results on a small set of images, they do show that information is present in license plates as small as 20 pixels in width. Building on these ideas, in this paper we propose to train a CNN for recognizing highly degraded license plates with an unknown background template, font type, size and character location. We also explicitly work in the presence of high amounts of additive noise – a common occurrence in real-world imagery. Recognition by Human Observers We begin by performing a perceptual study to determine how well human observers can decipher degraded license plates. This study provides a baseline against which to compare our computational approaches. Observers were shown images of synthetically Figure 1. An example of the type of degraded license plate that we seek to
Prognostics and health management using physics-of-failure
(2008)

This paper presents a physics-of-failure based prognostics and health management approach for effective reliability prediction. This method permits in-situ assessment of system reliability under actual application conditions. The method uses sensor data with models that enable in-situ assessment of the deviation or degradation of a product from an expected normal operating condition (i.e., the system's ldquohealthrdquo) and the prediction of the future state of reliability. The implementation procedure of this approach includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty.
Attend More Times for Image Captioning
(2018)

Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.
Deep Learning Human Mind for Automated Visual Classification
(2017)

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)&#x2013;based regressor to project images onto the learned manifold, thus allowing machines to employ human brain&#x2013;based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain&#x2013;driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets.